import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	Any         =    logging.get_logger(__name__)

__a    :	Tuple         =    {
    """microsoft/unispeech-sat-base-100h-libri-ft""": (
        """https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"""
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					int     					=		'''unispeech-sat'''





			def __init__(  self ,  lowerCAmelCase__=32 ,  lowerCAmelCase__=7_68 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=30_72 ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=1E-5 ,  lowerCAmelCase__="group" ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) ,  lowerCAmelCase__=(5, 2, 2, 2, 2, 2, 2) ,  lowerCAmelCase__=(10, 3, 3, 3, 3, 2, 2) ,  lowerCAmelCase__=False ,  lowerCAmelCase__=1_28 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=True ,  lowerCAmelCase__=0.05 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=10 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=3_20 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=1_00 ,  lowerCAmelCase__=2_56 ,  lowerCAmelCase__=2_56 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__="mean" ,  lowerCAmelCase__=False ,  lowerCAmelCase__=False ,  lowerCAmelCase__=2_56 ,  lowerCAmelCase__=(5_12, 5_12, 5_12, 5_12, 15_00) ,  lowerCAmelCase__=(5, 3, 3, 1, 1) ,  lowerCAmelCase__=(1, 2, 3, 1, 1) ,  lowerCAmelCase__=5_12 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=2 ,  lowerCAmelCase__=5_04 ,  **lowerCAmelCase__ ,  )	->   Any:

							'''simple docstring'''


							super().__init__(**lowerCAmelCase__ ,  pad_token_id=lowerCAmelCase__ ,  bos_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__		)
							__lowercase					     =				hidden_size
							__lowercase					     =				feat_extract_norm
							__lowercase					     =				feat_extract_activation
							__lowercase					     =				list(lowerCAmelCase__		)
							__lowercase					     =				list(lowerCAmelCase__		)
							__lowercase					     =				list(lowerCAmelCase__		)
							__lowercase					     =				conv_bias
							__lowercase					     =				num_conv_pos_embeddings
							__lowercase					     =				num_conv_pos_embedding_groups
							__lowercase					     =				len(self.conv_dim		)
							__lowercase					     =				num_hidden_layers
							__lowercase					     =				intermediate_size
							__lowercase					     =				hidden_act
							__lowercase					     =				num_attention_heads
							__lowercase					     =				hidden_dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				feat_proj_dropout
							__lowercase					     =				final_dropout
							__lowercase					     =				layerdrop
							__lowercase					     =				layer_norm_eps
							__lowercase					     =				initializer_range
							__lowercase					     =				vocab_size
							__lowercase					     =				num_clusters
							__lowercase					     =				do_stable_layer_norm
							__lowercase					     =				use_weighted_layer_sum

							if (
							    (len(self.conv_stride		) != self.num_feat_extract_layers)
							    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
							    or (len(self.conv_dim		) != self.num_feat_extract_layers)
							):
											raise ValueError(
											    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
											    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
											    F" {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,"
											    F" `len(config.conv_kernel) = {len(self.conv_kernel		)}`."		)

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							__lowercase					     =				apply_spec_augment
							__lowercase					     =				mask_time_prob
							__lowercase					     =				mask_time_length
							__lowercase					     =				mask_time_min_masks
							__lowercase					     =				mask_feature_prob
							__lowercase					     =				mask_feature_length
							__lowercase					     =				mask_feature_min_masks

							# parameters for pretraining with codevector quantized representations
							__lowercase					     =				num_codevectors_per_group
							__lowercase					     =				num_codevector_groups
							__lowercase					     =				contrastive_logits_temperature
							__lowercase					     =				feat_quantizer_dropout
							__lowercase					     =				num_negatives
							__lowercase					     =				codevector_dim
							__lowercase					     =				proj_codevector_dim
							__lowercase					     =				diversity_loss_weight

							# ctc loss
							__lowercase					     =				ctc_loss_reduction
							__lowercase					     =				ctc_zero_infinity

							# SequenceClassification-specific parameter. Feel free to ignore for other classes.
							__lowercase					     =				classifier_proj_size

							# XVector-specific parameters. Feel free to ignore for other classes.
							__lowercase					     =				list(lowerCAmelCase__		)
							__lowercase					     =				list(lowerCAmelCase__		)
							__lowercase					     =				list(lowerCAmelCase__		)
							__lowercase					     =				xvector_output_dim





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							return functools.reduce(operator.mul ,  self.conv_stride ,  1		)
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)